Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations781370
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory172.9 MiB
Average record size in memory232.0 B

Variable types

Numeric6
Categorical19
Text4

Alerts

WaitTime has constant value "0"Constant
UsedCPUTime has constant value "-1.0"Constant
ReqTime has constant value "-1.0"Constant
ReqMemory has constant value "-1.0"Constant
Status has constant value "-1"Constant
ExecutableID has constant value "-1"Constant
PartitionID has constant value "-1"Constant
JobStructure has constant value "-1"Constant
JobStructureParams has constant value "-1"Constant
UsedNetwork has constant value "-1.0"Constant
UsedLocalDiskSpace has constant value "-1.0"Constant
UsedResources has constant value "-1"Constant
ReqPlatform has constant value "-1"Constant
ReqNetwork has constant value "-1.0"Constant
ReqLocalDiskSpace has constant value "-1.0"Constant
ReqResources has constant value "-1"Constant
VOID has constant value "-1"Constant
ProjectID has constant value "-1"Constant
JobID is highly overall correlated with SubmitTimeHigh correlation
NProc is highly overall correlated with ReqNProcsHigh correlation
ReqNProcs is highly overall correlated with NProcHigh correlation
SubmitTime is highly overall correlated with JobIDHigh correlation
NProc is highly skewed (γ1 = 23.91612379)Skewed
ReqNProcs is highly skewed (γ1 = 23.91612379)Skewed
JobID is uniformly distributedUniform
JobID has unique valuesUnique

Reproduction

Analysis started2024-08-21 13:08:02.599108
Analysis finished2024-08-21 13:10:59.714091
Duration2 minutes and 57.11 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

JobID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct781370
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean390685.5
Minimum1
Maximum781370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:00.418531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile39069.45
Q1195343.25
median390685.5
Q3586027.75
95-th percentile742301.55
Maximum781370
Range781369
Interquartile range (IQR)390684.5

Descriptive statistics

Standard deviation225562.23
Coefficient of variation (CV)0.5773499
Kurtosis-1.2
Mean390685.5
Median Absolute Deviation (MAD)195342.5
Skewness3.4897883 × 10-15
Sum3.0526993 × 1011
Variance5.0878322 × 1010
MonotonicityStrictly increasing
2024-08-21T15:11:01.352156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
520917 1
 
< 0.1%
520908 1
 
< 0.1%
520909 1
 
< 0.1%
520910 1
 
< 0.1%
520911 1
 
< 0.1%
520912 1
 
< 0.1%
520913 1
 
< 0.1%
520914 1
 
< 0.1%
520915 1
 
< 0.1%
Other values (781360) 781360
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
781370 1
< 0.1%
781369 1
< 0.1%
781368 1
< 0.1%
781367 1
< 0.1%
781366 1
< 0.1%
781365 1
< 0.1%
781364 1
< 0.1%
781363 1
< 0.1%
781362 1
< 0.1%
781361 1
< 0.1%

SubmitTime
Real number (ℝ)

HIGH CORRELATION 

Distinct744113
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1185454 × 109
Minimum1.0466803 × 109
Maximum1.1461511 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:02.273585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0466803 × 109
5-th percentile1.0933445 × 109
Q11.1023609 × 109
median1.116872 × 109
Q31.1354612 × 109
95-th percentile1.1438712 × 109
Maximum1.1461511 × 109
Range99470826
Interquartile range (IQR)33100359

Descriptive statistics

Standard deviation17978184
Coefficient of variation (CV)0.016072824
Kurtosis-0.77277012
Mean1.1185454 × 109
Median Absolute Deviation (MAD)15721318
Skewness-0.24057166
Sum8.7399785 × 1014
Variance3.2321511 × 1014
MonotonicityNot monotonic
2024-08-21T15:11:03.109681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1133884861 17
 
< 0.1%
1133884515 16
 
< 0.1%
1133885340 15
 
< 0.1%
1145004020 15
 
< 0.1%
1133885315 15
 
< 0.1%
1145363118 13
 
< 0.1%
1145004021 13
 
< 0.1%
1128511694 12
 
< 0.1%
1119963608 11
 
< 0.1%
1123768128 11
 
< 0.1%
Other values (744103) 781232
> 99.9%
ValueCountFrequency (%)
1046680270 1
< 0.1%
1050208892 1
< 0.1%
1050211992 1
< 0.1%
1063172000 1
< 0.1%
1063172002 1
< 0.1%
1063172004 1
< 0.1%
1063172077 1
< 0.1%
1063172315 1
< 0.1%
1063172355 1
< 0.1%
1063180046 1
< 0.1%
ValueCountFrequency (%)
1146151096 1
 
< 0.1%
1146146782 1
 
< 0.1%
1146146072 1
 
< 0.1%
1146146054 1
 
< 0.1%
1146144040 1
 
< 0.1%
1146144039 2
< 0.1%
1146144017 3
< 0.1%
1146144016 1
 
< 0.1%
1146143995 1
 
< 0.1%
1146143994 2
< 0.1%

WaitTime
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0
781370 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters781370
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 781370
100.0%

Length

2024-08-21T15:11:03.806017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:04.348375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
0 781370
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 781370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 781370
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 781370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 781370
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 781370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 781370
100.0%

RunTime
Real number (ℝ)

Distinct197854
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89273.912
Minimum-6471
Maximum18071901
Zeros1840
Zeros (%)0.2%
Negative15
Negative (%)< 0.1%
Memory size6.0 MiB
2024-08-21T15:11:04.970090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-6471
5-th percentile73
Q11243
median12076
Q372000
95-th percentile418453.2
Maximum18071901
Range18078372
Interquartile range (IQR)70757

Descriptive statistics

Standard deviation284299.69
Coefficient of variation (CV)3.1845775
Kurtosis689.99731
Mean89273.912
Median Absolute Deviation (MAD)11963
Skewness19.919738
Sum6.9755956 × 1010
Variance8.0826312 × 1010
MonotonicityNot monotonic
2024-08-21T15:11:05.815320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864000 6683
 
0.9%
36000 6138
 
0.8%
60 5627
 
0.7%
3660 5626
 
0.7%
3600 5476
 
0.7%
54000 5319
 
0.7%
1 2836
 
0.4%
12000 2742
 
0.4%
72000 2437
 
0.3%
13200 2019
 
0.3%
Other values (197844) 736467
94.3%
ValueCountFrequency (%)
-6471 1
< 0.1%
-3417 1
< 0.1%
-3194 1
< 0.1%
-2882 1
< 0.1%
-2775 1
< 0.1%
-2373 1
< 0.1%
-2278 1
< 0.1%
-2142 1
< 0.1%
-325 1
< 0.1%
-159 1
< 0.1%
ValueCountFrequency (%)
18071901 1
< 0.1%
18071899 1
< 0.1%
18071858 1
< 0.1%
18071268 1
< 0.1%
18070477 1
< 0.1%
15138878 1
< 0.1%
14778046 1
< 0.1%
14768526 1
< 0.1%
14767545 1
< 0.1%
14767340 1
< 0.1%

NProc
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0732611
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:06.604551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum64
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.273685
Coefficient of variation (CV)1.186743
Kurtosis675.43635
Mean1.0732611
Median Absolute Deviation (MAD)0
Skewness23.916124
Sum838614
Variance1.6222735
MonotonicityNot monotonic
2024-08-21T15:11:07.377210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 776089
99.3%
4 1170
 
0.1%
2 801
 
0.1%
8 569
 
0.1%
16 515
 
0.1%
32 334
 
< 0.1%
6 251
 
< 0.1%
12 210
 
< 0.1%
10 160
 
< 0.1%
18 129
 
< 0.1%
Other values (34) 1142
 
0.1%
ValueCountFrequency (%)
1 776089
99.3%
2 801
 
0.1%
3 115
 
< 0.1%
4 1170
 
0.1%
5 83
 
< 0.1%
6 251
 
< 0.1%
7 27
 
< 0.1%
8 569
 
0.1%
9 33
 
< 0.1%
10 160
 
< 0.1%
ValueCountFrequency (%)
64 3
 
< 0.1%
61 1
 
< 0.1%
60 3
 
< 0.1%
51 19
 
< 0.1%
50 31
< 0.1%
48 61
< 0.1%
41 17
 
< 0.1%
40 42
< 0.1%
36 21
 
< 0.1%
35 14
 
< 0.1%

UsedCPUTime
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1.0
781370 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3125480
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 781370
100.0%

Length

2024-08-21T15:11:08.100450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:08.621097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

UsedMemory
Real number (ℝ)

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199888.45
Minimum-1
Maximum2147483.6
Zeros0
Zeros (%)0.0%
Negative505226
Negative (%)64.7%
Memory size6.0 MiB
2024-08-21T15:11:09.249168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3524288
95-th percentile838860.8
Maximum2147483.6
Range2147484.6
Interquartile range (IQR)524289

Descriptive statistics

Standard deviation306557.32
Coefficient of variation (CV)1.533642
Kurtosis2.3549138
Mean199888.45
Median Absolute Deviation (MAD)0
Skewness1.4870091
Sum1.5618684 × 1011
Variance9.3977391 × 1010
MonotonicityNot monotonic
2024-08-21T15:11:10.011085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
-1 505226
64.7%
524288 107260
 
13.7%
536870.912 89640
 
11.5%
838860.8 32297
 
4.1%
104857.6 15918
 
2.0%
402653.184 8716
 
1.1%
1153433.6 3290
 
0.4%
33554.432 2469
 
0.3%
1073741.824 2246
 
0.3%
1048576 2049
 
0.3%
Other values (35) 12259
 
1.6%
ValueCountFrequency (%)
-1 505226
64.7%
1048.576 3
 
< 0.1%
1677.7216 1
 
< 0.1%
8388.608 39
 
< 0.1%
10485.76 1
 
< 0.1%
15728.64 3
 
< 0.1%
26214.4 1
 
< 0.1%
33554.432 2469
 
0.3%
52428.8 388
 
< 0.1%
62914.56 6
 
< 0.1%
ValueCountFrequency (%)
2147483.648 3
 
< 0.1%
2097152 5
 
< 0.1%
1887436.8 1
 
< 0.1%
1782579.2 2030
0.3%
1677721.6 1
 
< 0.1%
1585446.912 106
 
< 0.1%
1572864 1800
0.2%
1468006.4 1
 
< 0.1%
1363148.8 1827
0.2%
1342177.28 9
 
< 0.1%

ReqNProcs
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0732611
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:11.060007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum64
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.273685
Coefficient of variation (CV)1.186743
Kurtosis675.43635
Mean1.0732611
Median Absolute Deviation (MAD)0
Skewness23.916124
Sum838614
Variance1.6222735
MonotonicityNot monotonic
2024-08-21T15:11:11.834693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 776089
99.3%
4 1170
 
0.1%
2 801
 
0.1%
8 569
 
0.1%
16 515
 
0.1%
32 334
 
< 0.1%
6 251
 
< 0.1%
12 210
 
< 0.1%
10 160
 
< 0.1%
18 129
 
< 0.1%
Other values (34) 1142
 
0.1%
ValueCountFrequency (%)
1 776089
99.3%
2 801
 
0.1%
3 115
 
< 0.1%
4 1170
 
0.1%
5 83
 
< 0.1%
6 251
 
< 0.1%
7 27
 
< 0.1%
8 569
 
0.1%
9 33
 
< 0.1%
10 160
 
< 0.1%
ValueCountFrequency (%)
64 3
 
< 0.1%
61 1
 
< 0.1%
60 3
 
< 0.1%
51 19
 
< 0.1%
50 31
< 0.1%
48 61
< 0.1%
41 17
 
< 0.1%
40 42
< 0.1%
36 21
 
< 0.1%
35 14
 
< 0.1%

ReqTime
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1.0
781370 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3125480
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 781370
100.0%

Length

2024-08-21T15:11:12.538343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:13.062303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

ReqMemory
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1.0
781370 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3125480
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 781370
100.0%

Length

2024-08-21T15:11:13.611890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:14.127156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Status
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:14.668082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:15.181895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

UserID
Text

Distinct387
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:16.758523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1620909
Min length2

Characters and Unicode

Total characters2470763
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)< 0.1%

Sample

1st rowU0
2nd rowU0
3rd rowU0
4th rowU0
5th rowU0
ValueCountFrequency (%)
u3 156706
20.1%
u200 132112
16.9%
u204 67164
 
8.6%
u1 47213
 
6.0%
u73 43475
 
5.6%
u203 37639
 
4.8%
u20 30538
 
3.9%
u7 26105
 
3.3%
u31 24771
 
3.2%
u105 16599
 
2.1%
Other values (377) 199048
25.5%
2024-08-21T15:11:19.353010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 781370
31.6%
0 453368
18.3%
2 362762
14.7%
3 314381
12.7%
1 186533
 
7.5%
4 104505
 
4.2%
7 96842
 
3.9%
9 56950
 
2.3%
5 48875
 
2.0%
8 35681
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2470763
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 781370
31.6%
0 453368
18.3%
2 362762
14.7%
3 314381
12.7%
1 186533
 
7.5%
4 104505
 
4.2%
7 96842
 
3.9%
9 56950
 
2.3%
5 48875
 
2.0%
8 35681
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2470763
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 781370
31.6%
0 453368
18.3%
2 362762
14.7%
3 314381
12.7%
1 186533
 
7.5%
4 104505
 
4.2%
7 96842
 
3.9%
9 56950
 
2.3%
5 48875
 
2.0%
8 35681
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2470763
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 781370
31.6%
0 453368
18.3%
2 362762
14.7%
3 314381
12.7%
1 186533
 
7.5%
4 104505
 
4.2%
7 96842
 
3.9%
9 56950
 
2.3%
5 48875
 
2.0%
8 35681
 
1.4%
Distinct107
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:20.199164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.6667187
Min length2

Characters and Unicode

Total characters2083694
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowG0
2nd rowG0
3rd rowG0
4th rowG0
5th rowG0
ValueCountFrequency (%)
g2 197289
25.2%
g43 132331
16.9%
g27 89777
11.5%
g69 67164
 
8.6%
g4 38116
 
4.9%
g68 37639
 
4.8%
g12 25311
 
3.2%
g40 22392
 
2.9%
g22 21961
 
2.8%
g15 16304
 
2.1%
Other values (97) 133086
17.0%
2024-08-21T15:11:21.775280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 781370
37.5%
2 370989
17.8%
4 204314
 
9.8%
3 162839
 
7.8%
6 138288
 
6.6%
7 121164
 
5.8%
9 97895
 
4.7%
1 74293
 
3.6%
5 47817
 
2.3%
0 42621
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2083694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 781370
37.5%
2 370989
17.8%
4 204314
 
9.8%
3 162839
 
7.8%
6 138288
 
6.6%
7 121164
 
5.8%
9 97895
 
4.7%
1 74293
 
3.6%
5 47817
 
2.3%
0 42621
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2083694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 781370
37.5%
2 370989
17.8%
4 204314
 
9.8%
3 162839
 
7.8%
6 138288
 
6.6%
7 121164
 
5.8%
9 97895
 
4.7%
1 74293
 
3.6%
5 47817
 
2.3%
0 42621
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2083694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 781370
37.5%
2 370989
17.8%
4 204314
 
9.8%
3 162839
 
7.8%
6 138288
 
6.6%
7 121164
 
5.8%
9 97895
 
4.7%
1 74293
 
3.6%
5 47817
 
2.3%
0 42621
 
2.0%

ExecutableID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:22.421404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:22.955073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

QueueID
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Q0
128235 
Q15
91979 
Q5
89057 
Q10
76354 
Q18
66218 
Other values (45)
329527 

Length

Max length3
Median length3
Mean length2.5917645
Min length2

Characters and Unicode

Total characters2025127
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowQ0
2nd rowQ0
3rd rowQ0
4th rowQ0
5th rowQ0

Common Values

ValueCountFrequency (%)
Q0 128235
16.4%
Q15 91979
11.8%
Q5 89057
11.4%
Q10 76354
 
9.8%
Q18 66218
 
8.5%
Q32 37925
 
4.9%
Q16 28931
 
3.7%
Q11 26916
 
3.4%
Q8 23569
 
3.0%
Q7 19231
 
2.5%
Other values (40) 192955
24.7%

Length

2024-08-21T15:11:23.524293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
q0 128235
16.4%
q15 91979
11.8%
q5 89057
11.4%
q10 76354
 
9.8%
q18 66218
 
8.5%
q32 37925
 
4.9%
q16 28931
 
3.7%
q11 26916
 
3.4%
q8 23569
 
3.0%
q7 19231
 
2.5%
Other values (40) 192955
24.7%

Most occurring characters

ValueCountFrequency (%)
Q 781370
38.6%
1 388970
19.2%
0 215107
 
10.6%
5 190631
 
9.4%
3 122017
 
6.0%
8 111493
 
5.5%
2 82689
 
4.1%
9 43824
 
2.2%
6 40377
 
2.0%
7 24514
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2025127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 781370
38.6%
1 388970
19.2%
0 215107
 
10.6%
5 190631
 
9.4%
3 122017
 
6.0%
8 111493
 
5.5%
2 82689
 
4.1%
9 43824
 
2.2%
6 40377
 
2.0%
7 24514
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2025127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 781370
38.6%
1 388970
19.2%
0 215107
 
10.6%
5 190631
 
9.4%
3 122017
 
6.0%
8 111493
 
5.5%
2 82689
 
4.1%
9 43824
 
2.2%
6 40377
 
2.0%
7 24514
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2025127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 781370
38.6%
1 388970
19.2%
0 215107
 
10.6%
5 190631
 
9.4%
3 122017
 
6.0%
8 111493
 
5.5%
2 82689
 
4.1%
9 43824
 
2.2%
6 40377
 
2.0%
7 24514
 
1.2%

PartitionID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:24.153743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:24.698037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%
Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:25.519743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length98
Median length86
Mean length24.874835
Min length10

Characters and Unicode

Total characters19436450
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowgrid.uio.no
2nd rowgrid.uio.no
3rd rowgrid.uio.no
4th rowgrid.uio.no
5th rowgrid.uio.no
ValueCountFrequency (%)
bluesmoke.nsc.liu.se 98828
 
12.6%
hagrid.it.uu.se 84819
 
10.9%
ingrid.hpc2n.umu.se 66256
 
8.5%
sigrid.lunarc.lu.se 58477
 
7.5%
benedict.grid.aau.dk 56766
 
7.3%
farm.hep.lu.se 42410
 
5.4%
morpheus.dcgc.dk 34290
 
4.4%
c=si/o=signet/o=ijs/ou=f9/cn=brenta.ijs.si/sn=14 26604
 
3.4%
sg-access.pdc.kth.se 25017
 
3.2%
c=si/o=signet/o=ijs/ou=f9/cn=pikolit.ijs.si/sn=35 24422
 
3.1%
Other values (65) 263481
33.7%
2024-08-21T15:11:27.298095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19436450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19436450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19436450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%
Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2024-08-21T15:11:28.253553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length98
Median length86
Mean length24.874835
Min length10

Characters and Unicode

Total characters19436450
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowgrid.uio.no
2nd rowgrid.uio.no
3rd rowgrid.uio.no
4th rowgrid.uio.no
5th rowgrid.uio.no
ValueCountFrequency (%)
bluesmoke.nsc.liu.se 98828
 
12.6%
hagrid.it.uu.se 84819
 
10.9%
ingrid.hpc2n.umu.se 66256
 
8.5%
sigrid.lunarc.lu.se 58477
 
7.5%
benedict.grid.aau.dk 56766
 
7.3%
farm.hep.lu.se 42410
 
5.4%
morpheus.dcgc.dk 34290
 
4.4%
c=si/o=signet/o=ijs/ou=f9/cn=brenta.ijs.si/sn=14 26604
 
3.4%
sg-access.pdc.kth.se 25017
 
3.2%
c=si/o=signet/o=ijs/ou=f9/cn=pikolit.ijs.si/sn=35 24422
 
3.1%
Other values (65) 263481
33.7%
2024-08-21T15:11:29.979215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19436450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19436450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19436450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2219012
 
11.4%
i 1490431
 
7.7%
e 1206411
 
6.2%
s 1203945
 
6.2%
u 1036755
 
5.3%
/ 799603
 
4.1%
= 780445
 
4.0%
a 721727
 
3.7%
c 719759
 
3.7%
r 716818
 
3.7%
Other values (51) 8541544
43.9%

JobStructure
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:30.639436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:31.168244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

JobStructureParams
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:31.693893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:32.280388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

UsedNetwork
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1.0
781370 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3125480
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 781370
100.0%

Length

2024-08-21T15:11:32.801372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:33.307548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

UsedLocalDiskSpace
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1.0
781370 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3125480
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 781370
100.0%

Length

2024-08-21T15:11:33.839041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:34.352674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

UsedResources
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:34.879530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:35.384846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

ReqPlatform
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:35.916066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:36.413307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

ReqNetwork
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1.0
781370 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3125480
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 781370
100.0%

Length

2024-08-21T15:11:36.940429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:37.461264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

ReqLocalDiskSpace
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1.0
781370 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3125480
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.0
2nd row-1.0
3rd row-1.0
4th row-1.0
5th row-1.0

Common Values

ValueCountFrequency (%)
-1.0 781370
100.0%

Length

2024-08-21T15:11:38.001761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:38.524970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3125480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
25.0%
1 781370
25.0%
. 781370
25.0%
0 781370
25.0%

ReqResources
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:39.049757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:39.862470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

VOID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:40.377707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:40.911336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

ProjectID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
-1
781370 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1562740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 781370
100.0%

Length

2024-08-21T15:11:41.437491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-21T15:11:41.952694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 781370
100.0%

Most occurring characters

ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1562740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 781370
50.0%
1 781370
50.0%

Interactions

2024-08-21T15:10:37.519494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:14.598965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:19.603540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:23.966826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:28.560489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:33.140227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:38.340955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:15.658957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:20.355740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:24.776243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:29.363281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:33.944242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:39.097288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:16.482294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:21.054109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:25.513760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:30.074713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:34.663549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:39.870154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:17.283144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:21.815340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:26.321759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:30.905189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:35.425743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:40.637817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:18.104349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:22.539068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:27.087274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:31.708417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:36.124700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:41.339155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:18.837537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:23.216314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:27.828225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:32.426855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-21T15:10:36.805047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-21T15:11:42.298614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
JobIDNProcQueueIDReqNProcsRunTimeSubmitTimeUsedMemory
JobID1.000-0.0040.323-0.004-0.2501.000-0.278
NProc-0.0041.0000.0341.000-0.005-0.004-0.059
QueueID0.3230.0341.0000.0340.0900.3130.380
ReqNProcs-0.0041.0000.0341.000-0.005-0.004-0.059
RunTime-0.250-0.0050.090-0.0051.000-0.251-0.036
SubmitTime1.000-0.0040.313-0.004-0.2511.000-0.279
UsedMemory-0.278-0.0590.380-0.059-0.036-0.2791.000

Missing values

2024-08-21T15:10:44.004709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-21T15:10:50.670401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

JobIDSubmitTimeWaitTimeRunTimeNProcUsedCPUTimeUsedMemoryReqNProcsReqTimeReqMemoryStatusUserIDGroupIDExecutableIDQueueIDPartitionIDOrigSiteIDLastRunSiteIDJobStructureJobStructureParamsUsedNetworkUsedLocalDiskSpaceUsedResourcesReqPlatformReqNetworkReqLocalDiskSpaceReqResourcesVOIDProjectID
01104668027002181-1.0-1.0001-1.0-1.0-1U0G0-1Q0-1grid.uio.nogrid.uio.no-1-1-1.0-1.0-1-1-1.0-1.0-1-1-1
121050208892011071-1.0-1.0001-1.0-1.0-1U0G0-1Q0-1grid.uio.nogrid.uio.no-1-1-1.0-1.0-1-1-1.0-1.0-1-1-1
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